Search Results (178)

To address edge contact in non-circular gears arising from installation errors, a modification strategy represented by elliptical gears and driven by an ease-off topological surface is proposed. A tooth surface model for non-circular gears was first derived from meshing theory. The modification magnitude was defined using a second-order ease-off differential surface, and the modified surface is represented through non-uniform rational B-spline (NURBS) fitting. A tooth contact analysis (TCA) model is then built to evaluate how installation errors and modification amount influence contact behavior. The results indicate that an increase in center distance error reduces the contact ratio. For equal perturbations of axial horizontal and axial vertical mounting angles, the horizontal error has the stronger impact on the size and location of the contact patch. As the longitudinal modification coefficient grows, the contact path and peak pressure position shift from the tooth edge toward the mid-width; the contact ellipse first enlarges and then shrinks, while the contact pressure shows the opposite trend. The elastic deformation of the tooth surface increases with the mounting angle. Transmission tests confirm that the proposed modification lowers the transmission error relative to the unmodified gear pair. Full article

The present study proposes an integrated real-time energy management strategy (EMS) that combines an adaptive neuro-fuzzy inference system (ANFIS) with dynamic programming (DP) to enhance the energy efficiency and system durability of fuel cell electric commercial vehicles (FCECVs). Firstly, a comprehensive DP framework was established to optimize the EMS offline, which simultaneously considers power allocation and automated manual transmission (AMT) gear-shifting to minimize hydrogen consumption (HC). Then, the DP framework was employed to determine optimal power allocation patterns of the FCECVs under various initial state-of-charge (SOC) battery conditions. Based on the DP results, a novel real-time EMS integrating ANFIS with DP solution was developed to formulate an efficient fuzzy inference system (FIS), where the ANFIS model was trained using the particle swarm optimization (PSO) algorithm. The proposed ANFIS-DP EMS was evaluated through extensive simulations under stochastic driving cycles, with performance comparisons against both the DP method and conventional charge-depleting and charge-sustaining (CD-CS) strategies. The experimental results demonstrate that the ANFIS-DP maintains efficient FCS operation across diverse driving conditions while effectively controlling the rate of power change within optimal ranges. Compared to the CD-CS strategy, the proposed method achieves a substantial 14.98% reduction in HC, approaching the performance of DP (only 5.40% higher). Most notably, the ANFIS-DP strategy demonstrates remarkable computational efficiency improvements, outperforming DP by 96.13% and CD-CS by 22.05%. These findings collectively validate the effectiveness of our proposed approach in achieving real-time energy management optimization for FCECVs. Full article

This study focuses on the development and optimization of a Clutchless Automated-Manual Transmission (CLAMT) system for tractors, aiming to enhance performance and efficiency across diverse operating conditions. It explores the use of a dog clutch mechanism as a simpler, robust alternative to traditional synchronizers. The main objective is to replace complex transmission setups—often requiring up to 32 gear ratios—with a system that operates efficiently using only two gears, without sacrificing versatility. Smooth gear engagement, even under varying loads and terrains, is a key challenge addressed. To ensure this, a Vehicle Management Unit (VMU) manages gear shifts and actively synchronizes speeds. The system leverages steady torque delivery through control algorithms and modern hybrid/electric powertrain capabilities. Two algorithmic approaches are implemented, and their performance is evaluated through empirical testing. Results show improvements in system simplicity, transmission reliability, and overall operational efficiency. The proposed approach offers valuable insights for future agricultural drivetrains, highlighting the potential of dog clutch-based architectures in reducing mechanical complexity while maintaining functional performance. Full article

The operational phase of offshore wind farms, lasting up to 20–25 years, exceeds the construction phase in duration. The ecological effects of underwater noise demand serious consideration, necessitating urgent research into its acoustic characteristics. This review conducts a systematic analysis of measurements of underwater noise from operational offshore wind farms, considering the correlations between turbine noise and distance, wind speed, turbine power, and foundation type. Propagation distance is the most critical factor influencing the underwater sound pressure level (SPL) of wind turbines, exhibiting a negative correlation with the SPL, with an attenuation of approximately 20.4 dB/decade. In contrast, wind speed and turbine power show a positive correlation with the SPL, with increase rates of 18.5 dB/decade and 12.4 dB/decade, respectively. Further analysis shows that foundation type and drive technology also have a significant impact on underwater SPL. With technological innovation, specifically the upgrade from conventional geared drive to direct-drive technology, the level of underwater noise can be reduced by approximately 9 dB, with the primary peak frequency being shifted to a lower range. Moreover, significant variations in SPLs were noted with the utilization of various types of foundation structures, with monopile foundations exhibiting the highest SPLs of underwater noise. These conclusions have important reference value for the scientific assessment of the health of aquatic organisms and ecosystems. Full article

In the realm of sustainable public transportation, the integration of intelligent electric bus propulsion systems represents a novel and promising approach to reducing environmental impact—particularly through the mitigation of NOx emissions and overall exhaust pollutants. This emerging technology underscores the growing need for advanced drive control architectures that ensure not only operational safety and reliability but also compliance with increasingly stringent emissions standards. The present article introduces an innovative analysis of energy-optimized dual-drive electric propulsion systems, with a specific focus on their potential for real-world application in emission-conscious urban mobility. A detailed dynamic model of a dual-drive electric bus was developed in MATLAB Simulink, incorporating a Fuzzy Logic-based decision-making algorithm embedded within the Transmission Control Unit (TCU). The proposed control architecture includes a torque-limiting safety strategy designed to prevent motor overspeed conditions, thereby enhancing both efficiency and mechanical integrity. Furthermore, the system architecture enables supervisory override of the Fuzzy Inference System (FIS) during critical scenarios, such as gear-shifting transitions, allowing adaptive control refinement. The study addresses the unique control and coordination challenges inherent in dual-drive systems, particularly in relation to optimizing gear selection for reduced energy consumption and emissions. Key areas of investigation include maximizing efficiency along the motor torque–speed characteristic, maintaining vehicular dynamic stability, and minimizing thermally induced performance degradation. The thermal modeling approach is grounded in integral formulations capturing major loss contributors including copper, iron, and mechanical losses while also evaluating convective heat transfer mechanisms to improve cooling effectiveness. These insights confirm that advanced thermal management is not only vital for performance optimization but also plays a central role in supporting long-term strategies for emission reduction and clean, efficient public transportation. Full article

This paper presents Rain-Cloud Condensation Optimizer (RCCO), a nature-inspired metaheuristic that maps cloud microphysics to population-based search. Candidate solutions (“droplets”) evolve under a dual-attractor dynamic toward both a global leader and a rank-weighted cloud core, with time-decaying coefficients that progressively shift emphasis from exploration to exploitation. Diversity is preserved via domain-aware coalescence and opposition-based mirroring sampled within the coordinate-wise band defined by two parents. Rare heavy-tailed “turbulence gusts” (Cauchy perturbations) enable long jumps, while a wrap-and-reflect scheme enforces feasibility near the bounds. A sine-map initializer improves early coverage with negligible overhead. RCCO exposes a small hyperparameter set, and its per-iteration time and memory scale linearly with population size and problem dimension. RCOO has been compared with 21 state-of-the-art optimizers, over the CEC 2022 benchmark suite, where it achieves competitive to superior accuracy and stability, and achieves the top results over eight functions, including in high-dimensional regimes. We further demonstrate constrained, real-world effectiveness on five structural engineering problems—cantilever stepped beam, pressure vessel, planetary gear train, ten-bar planar truss, and three-bar truss. These results suggest that a hydrology-inspired search framework, coupled with simple state-dependent schedules, yields a robust, low-tuning optimizer for black-box, nonconvex problems. Full article

A reluctance actuator integrated into the double-sided dog clutch of a gearbox can significantly simplify the gear shifting system. However, its disadvantage is that an axial position sensor is required to shift the neutral gear. The sensor is placed in the aggressive environment of a gearbox and reduces the reliability of the entire system. Sensorless methods proposed in the literature deal with electrical machines or actuators with one degree of freedom (linear motion or rotation). In the dog clutch, the shift sleeve rotates and moves along its rotation axis simultaneously, moreover, the coil inductances are highly dependent not only on the axial position but also on the relative angular position between the shift sleeve teeth and the slots of its counterpart. This work proposes an original algorithm of sensorless control, which main novelty is the applicability for systems with two degrees of freedom, such as the considered actuator. The voltage induced in one of the coils and the prediction of the shift sleeve motion, which is based on the electromechanical model of the clutch, are used to control the currents. Not only an axial position sensor but also angular encoders are not required to apply the proposed method. The algorithm was tested both in simulations and experiments under different conditions. The results show that the proposed method allows to shift the neutral gear sensorless at different rotation speeds and different loads on the sleeve, regardless of what gearwheel is initially engaged. Full article

This article presents a gear selection methodology for electric vehicle powertrains employing two identical Permanent Magnet Synchronous Machines (PMSMs) arranged in a twin-drive configuration. Both machines are coupled through a shared output shaft and operate with coordinated torque–speed characteristics, enabling efficient utilization of the available gear stages. The proposed approach establishes a control-oriented drivetrain framework that incorporates mechanical dynamics together with real-time thermal states and loss mechanisms. Unlike conventional strategies, which rely mainly on static or speed-based shifting rules, the method integrates detailed thermal and electromagnetic loss modeling directly into the gear-shifting logic. By accounting for the dynamic thermal behavior of PMSMs under variable load conditions, the strategy aims to reduce cumulative drivetrain losses, including electromagnetic, thermal, and mechanical, while maintaining high efficiency. The methodology is implemented in a MATLAB/Simulink R2024a and LabVIEW 2024Q2 co-simulation environment, where thermal feedback and instantaneous efficiency metrics dynamically guide gear selection. Simulation results demonstrate measurable improvements in energy utilization, particularly under transient operating conditions. The resulting efficiency maps are broader and flatter, as the motors’ operating points are continuously shifted toward zones of optimal performance through adaptive gear ratio control. The novelty of this work lies in combining real-time loss modeling, thermal feedback, and coordinated gear management in a twin-motor system, validated through experimentally motivated efficiency maps. The findings highlight a scalable and dynamic control framework suitable for advanced electric vehicle architectures, supporting intelligent efficiency-oriented drivetrain strategies that enhance sustainability, thermal management, and system performance across diverse operating conditions. Full article

Although numerous studies have used ML to predict gear transmission error, few have provided a normalized, interpretable risk metric for early tolerance assessment. This work fills that gap by proposing the Tonal Risk Index (TRI). Kinematic Transmission Error (KTE) is a well-established primary excitation source of tonal gear noise in electric vehicle drivetrains. This study introduces the TRI, a novel, dimensionless indicator that quantifies relative tonal noise risk directly from predicted KTE values. We employ a large-scale dataset of 39,984 Monte Carlo simulations comprising 15 manufacturing tolerance and process-shift variables, with KTE values as the target. Baseline linear regression failed to capture the strongly non-linear relationships between tolerances and KTE (R² ≈ 0), whereas non-linear models—Random Forest and XGBoost—achieved high predictive accuracy (R² ≈ 0.82). Feature importance analysis revealed that pitch error, radial run-out, and misalignment are consistently the most influential parameters, with notable interaction effects such as pitch error × run-out and misalignment × form-defect shift. The TRI normalises predicted KTE values to a 0–1 scale, enabling rapid comparison of tolerance configurations in terms of tonal excitation risk. This approach supports early-stage design decision-making, reduces reliance on high-fidelity simulations and physical prototypes, and aligns with sustainability objectives by lowering material usage and energy consumption. The results demonstrate that data-driven surrogate models, combined with the TRI metric, can effectively bridge the gap between manufacturing tolerances and NVH performance assessment. Full article

Traditional single-species fisheries management has proven inadequate for capturing ecosystem interactions, leading to a shift toward ecosystem-based approaches. In Korea, diverse small- and medium-scale with varying gear types, production volumes, and practices require management tools that address both ecological and industrial needs. This study developed a Gear-based Fisheries Management Index (GFMI) for 24 coastal and offshore fisheries in Korea. The framework, based on the “ideal gear attributes” defined by ICES, is structured around three objectives: gear controllability, environmental sustainability, and operational functionality. Sub-indicators and weights were derived through expert consultation using the Analytic Hierarchy Process and standardized with Z-scores from national statistics, including production volume, license numbers, and accident rates. Results show that in coastal fisheries, coastal gillnets (61.7) and coastal improved stow nets (60.7) recorded the highest scores, largely due to negative impacts such as bycatch, reproductive capacity, and gear loss. Coastal purse seines (40.9) received the lowest score, reflecting species selectivity advantages. In offshore fisheries, large bottom pair trawls (71.8) and Southwestern medium-size bottom pair trawl (69.3) ranked highest, indicating strong habitat impacts. While coastal improved stow nets, large purse seines, and large trawls performed well in operational functionality, high costs and efficiency constraints remain key vulnerabilities. Full article

This study explores the application of profile shift coefficients as a design strategy to eliminate the need for stepped planet gears in a specific type of planetary gear train, referred to as the AA gear train. By appropriately selecting gear tooth numbers and applying compensating profile shifts to the two central gears, it is possible to equalize their diameters, enabling the use of simple single-step spur gears as planet gears. This significantly simplifies manufacturing, may improve power branching capabilities, and reduces the cost and volume. This paper outlines the geometric and functional limitations of this approach, including the practically allowable range of profile shift values and their impact on the tooth strength, contact ratio, and potential interference. Additionally, the influence of the planet count on assembly conditions and profile shift requirements is examined. The design may offer advantages in compactness and manufacturability (for moderate gear ratios) within a single stage. However, limitations in efficiency, power branching, and self-locking—especially at high ratios—must be considered. While the method provides a viable alternative to conventional stepped planet designs in certain cases, its applicability remains constrained by profile shift limitations and system-specific design compromises. Full article

The energy management strategy (EMS) is a core technology for improving the fuel economy of hybrid electric vehicles (HEVs). However, the coexistence of both discrete and continuous control variables, along with complex physical constraints in HEV powertrains, presents significant challenges for the design of efficient EMSs based on deep reinforcement learning (DRL). To further enhance fuel efficiency and coordinated powertrain control under complex driving conditions, this study proposes a hierarchical DRL-based EMS. The proposed strategy adopts a layered control architecture: the upper layer utilizes the soft actor–critic (SAC) algorithm for continuous control of engine torque, while the lower layer employs a deep Q-network (DQN) for discrete gear selection optimization. Through offline training and online simulation, experimental results demonstrate that the proposed strategy achieves fuel economy performance comparable to dynamic programming (DP), with only a 3.06% difference in fuel consumption. Moreover, it significantly improves computational efficiency, thereby enhancing the feasibility of real-time deployment. This study validates the optimization potential and real-time applicability of hierarchical reinforcement learning for hybrid control in HEV energy management. Furthermore, its adaptability is demonstrated through sustained and stable performance under long-duration, complex urban bus driving conditions. Full article

Fisheries management in Abu Dhabi has undergone a significant transformation over the past two decades, shifting from an open-access system to a more regulated framework aimed at stock recovery and sustainability. This study evaluates the status of 13 commercially important fish species—accounting for 95% of total landings—using two complementary stock assessment methods: CMSY++, a Bayesian catch-based model, and the Length-Converted Catch Curve (LCCC), a length-based mortality estimation approach. Fisheries-dependent and fisheries-independent data collected from 2001 to 2024 were analyzed to assess trends in biomass, exploitation rates, and spawning stock biomass per recruit (SBR). CMSY++ outputs indicate that in 2005, only 1 out of 13 stocks was sustainable, with biomass (B) above the biomass that can reproduce maximum sustainable yield (BMSY) and fishing mortality (F) below the fishing mortality that gives the maximum sustainable yield (FMSY), and 5 stocks were overexploited. By 2024, seven stocks had recovered to sustainable levels, with biomass at or above BMSY and exploitation rates below FMSY. LCCC results for 2024 further confirm these findings, with most species exhibiting SBR values above the 30% threshold, except for Lethrinus nebulosus (Forsskål, 1775), which remains close to overexploitation limits. The observed stock recovery coincides with effective governance and key fisheries management measures, including effort reduction, gear restrictions, and spatial protections. While most stocks are now within sustainable biological reference points, transboundary species such as Scomberomorus commerson (Lacépède, 1800) require continued regional cooperation for effective management. These findings contribute to ongoing efforts to achieve and maintain fully sustainable fisheries in the Arabian Gulf while aligning with international conservation frameworks, biodiversity protection goals, and climate-resilient fisheries management strategies. Full article

Energy management for hybrid electric commercial vehicles, involving continuous power output and discrete gear shifting, constitutes a typical mixed-integer programming (MIP) problem, presenting significant challenges for real-time performance and computational efficiency. To address this, this paper proposes a physics-informed neural network-optimized model predictive control (PINN-MPC) strategy. On one hand, this strategy simultaneously optimizes continuous and discrete states within the MPC framework to achieve the integrated objectives of minimizing fuel consumption, tracking speed, and managing battery state-of-charge (SOC). On the other hand, to overcome the prohibitively long solving time of the MIP-MPC, a physics-informed neural network (PINN) optimizer is designed. This optimizer employs the soft-argmax function to handle discrete gear variables and embeds system dynamics constraints using an augmented Lagrangian approach. Validated via hardware-in-the-loop (HIL) testing under two distinct real-world driving cycles, the results demonstrate that, compared to the open-source solver BONMIN, PINN-MPC significantly reduces computation time—dramatically decreasing the average solving time from approximately 10 s to about 5 ms—without sacrificing the combined vehicle dynamic and economic performance. Full article

This study investigates the use of a custom-built 10 GHz continuous wave micro-Doppler radar system to analyze external vibrations of idling vehicles under various conditions. Scenarios included different gear engagements with one occupant and parked gear with up to four occupants. Motivated by security concerns, such as the threat posed by idling vehicles with multiple occupants, the research explores how micro-Doppler signatures can indicate vehicle readiness to move. Experiments focused on a mid-size SUV, with similar trends seen in other vehicles. Radar data were compared to in situ accelerometer measurements, confirming that the radar system can detect subtle frequency changes, especially during gear shifts. The system’s sensitivity enables it to distinguish variations tied to gear state and passenger load. Extracted features like frequency and magnitude show strong potential for use in machine learning models, offering a non-invasive, remote sensing method for reliably identifying vehicle operational states and occupancy levels in security or monitoring contexts. Spectrogram and PSD analyses reveal consistent tonal vibrations around 30 Hz, tied to engine activity, with harmonics at 60 Hz and 90 Hz. Gear shifts produce impulse signatures primarily below 20 Hz, and transient data show distinct peaks at 50, 80, and 100 Hz. Key features at 23 Hz and 45 Hz effectively indicate engine and gear states. Radar and accelerometer data align well, supporting the potential for remote sensing and machine learning-based classification. Full article